Use batch transform to run inference with Amazon SageMaker - Amazon SageMaker

Use batch transform to run inference with Amazon SageMaker

Use batch transform when you need to do the following:

  • Preprocess datasets to remove noise or bias that interferes with training or inference from your dataset.

  • Get inferences from large datasets.

  • Run inference when you don't need a persistent endpoint.

  • Associate input records with inferences to help with the interpretation of results.

To filter input data before performing inferences or to associate input records with inferences about those records, see Associate Prediction Results with Input Records. For example, you can filter input data to provide context for creating and interpreting reports about the output data.

Use batch transform to get inferences from large datasets

Batch transform automatically manages the processing of large datasets within the limits of specified parameters. For example, having a dataset file, input1.csv, stored in an S3 bucket. The content of the input file might look like the following example.

Record1-Attribute1, Record1-Attribute2, Record1-Attribute3, ..., Record1-AttributeM Record2-Attribute1, Record2-Attribute2, Record2-Attribute3, ..., Record2-AttributeM Record3-Attribute1, Record3-Attribute2, Record3-Attribute3, ..., Record3-AttributeM ... RecordN-Attribute1, RecordN-Attribute2, RecordN-Attribute3, ..., RecordN-AttributeM

When a batch transform job starts, SageMaker starts compute instances and distributes the inference or preprocessing workload between them. Batch Transform partitions the Amazon S3 objects in the input by key and maps Amazon S3 objects to instances. When you have multiple files, one instance might process input1.csv, and another instance might process the file named input2.csv. If you have one input file but initialize multiple compute instances, only one instance processes the input file. The rest of the instances are idle.

You can also split input files into mini-batches. For example, you might create a mini-batch from input1.csv by including only two of the records.

Record3-Attribute1, Record3-Attribute2, Record3-Attribute3, ..., Record3-AttributeM Record4-Attribute1, Record4-Attribute2, Record4-Attribute3, ..., Record4-AttributeM

SageMaker processes each input file separately. It doesn't combine mini-batches from different input files to comply with the MaxPayloadInMB limit.

To split input files into mini-batches when you create a batch transform job, set the SplitType parameter value to Line. SageMaker uses the entire input file in a single request when:

  • SplitType is set to None.

  • An input file can't be split into mini-batches.

. Note that Batch Transform doesn't support CSV-formatted input that contains embedded newline characters. You can control the size of the mini-batches by using the BatchStrategy and MaxPayloadInMB parameters. MaxPayloadInMB must not be greater than 100 MB. If you specify the optional MaxConcurrentTransforms parameter, then the value of (MaxConcurrentTransforms * MaxPayloadInMB) must also not exceed 100 MB.

If the batch transform job successfully processes all of the records in an input file, it creates an output file. The output file has the same name and the .out file extension. For multiple input files, such as input1.csv and input2.csv, the output files are named input1.csv.out and input2.csv.out. The batch transform job stores the output files in the specified location in Amazon S3, such as s3://awsexamplebucket/output/.

The predictions in an output file are listed in the same order as the corresponding records in the input file. The output file input1.csv.out, based on the input file shown earlier, would look like the following.

Inference1-Attribute1, Inference1-Attribute2, Inference1-Attribute3, ..., Inference1-AttributeM Inference2-Attribute1, Inference2-Attribute2, Inference2-Attribute3, ..., Inference2-AttributeM Inference3-Attribute1, Inference3-Attribute2, Inference3-Attribute3, ..., Inference3-AttributeM ... InferenceN-Attribute1, InferenceN-Attribute2, InferenceN-Attribute3, ..., InferenceN-AttributeM

If you set SplitType to Line, you can set the AssembleWith parameter to Line to concatenate the output records with a line delimiter. This does not change the number of output files. The number of output files is equal to the number of input files, and using AssembleWith does not merge files. If you don't specify the AssembleWith parameter, the output records are concatenated in a binary format by default.

When the input data is very large and is transmitted using HTTP chunked encoding, to stream the data to the algorithm, set MaxPayloadInMB to 0. Amazon SageMaker built-in algorithms don't support this feature.

For information about using the API to create a batch transform job, see the CreateTransformJob API. For more information about the relationship between batch transform input and output objects, see OutputDataConfig. For an example of how to use batch transform, see (Optional) Make Prediction with Batch Transform.

Speed up a batch transform job

If you are using the CreateTransformJob API, you can reduce the time it takes to complete batch transform jobs by using optimal values for parameters. This includes parameters such as MaxPayloadInMB, MaxConcurrentTransforms, or BatchStrategy. The ideal value for MaxConcurrentTransforms is equal to the number of compute workers in the batch transform job.

If you are using the SageMaker console, specify these optimal parameter values in the Additional configuration section of the Batch transform job configuration page. SageMaker automatically finds the optimal parameter settings for built-in algorithms. For custom algorithms, provide these values through an execution-parameters endpoint.

Use batch transform to test production variants

To test different models or hyperparameter settings, create a separate transform job for each new model variant and use a validation dataset. For each transform job, specify a unique model name and location in Amazon S3 for the output file. To analyze the results, use Inference Pipeline Logs and Metrics.

Batch transform sample notebooks

For sample notebook that uses batch transform, see Batch Transform with PCA and DBSCAN Movie Clusters. This notebook uses batch transform with a principal component analysis (PCA) model to reduce data in a user-item review matrix. It then shows the application of a density-based spatial clustering of applications with noise (DBSCAN) algorithm to cluster movies.

For instructions on creating and accessing Jupyter notebook instances that you can use to run the example in SageMaker, see Amazon SageMaker Notebook Instances. After creating and opening a notebook instance, choose the SageMaker Examples tab to see a list of all the SageMaker examples. The topic modeling example notebooks that use the NTM algorithms are located in the Advanced functionality section. To open a notebook, choose its Use tab, then choose Create copy.